Annals of Operations Research

, Volume 187, Issue 1, pp 45–63 | Cite as

Nuclear threat detection with mobile distributed sensor networks

Open Access
Article

Abstract

The ability to track illicit radioactive source in an urban environment is critical in national security applications. To this end, two modes of operation are common: positioning individual portal monitors, and deploying a network of distributed sensors. We address here the use of multiple detectors, mounted on moving vehicles, for the purpose of detecting nuclear threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors’ positions are known in real-time as these are continuously reported from GPS data. The level of detected risk is then reported from each detector at each position. The problem is to delineate the presence of a potentially dangerous source and its approximate location by identifying a small area that has an elevated concentration of reported risk. This problem of using spatially deployed mobile detector networks to identify and locate risks is modeled and formulated here. The problem is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enable its use in real time, and in areas containing a large number of deployed detectors. A simulation study, that takes into account false-positive and false-negatives reports from individual sensors, demonstrates the effectiveness of the algorithm in using the sensor network’s detection capabilities.

Nuclear threat detection Network flow Parametric cut Counter-terrorism Discrete event simulation 

References

  1. Attix, F. H. (1986). Introduction to radiological physics and radiation dosimetry. New York: Wiley. CrossRefGoogle Scholar
  2. Berger, M. J., Hubbell, J. H., Seltzer, S. M., Chang, J., Coursey, J. S., Sukumar, R., & Zucker, D. S. (1998). Xcom: Photon cross sections database. Standard reference database 8 (xgam). National Institute of Standards and Technology. Google Scholar
  3. Brennan, S. M., Mielke, A. M., Torney, D. C., & MacCabe, A. B. (2004). Radiation detection with distributed sensor networks. Computer, 37(8), 57–59. CrossRefGoogle Scholar
  4. Brennan, S. M., Mielke, A. M., & Torney, D. C. (2005). Radioactive source detection by sensor networks. IEEE Transactions on Nuclear Science, 52(3), 813–819. CrossRefGoogle Scholar
  5. Chandran, B., & Hochbaum, D. S. (2009). Pseudoflow solver. http://riot.ieor.berkeley.edu/riot/Applications/Pseudoflow/maxflow.html.
  6. Cunningham, C. T. (1995). Detecting and track of a stochastic target using multiple measurements (Technical Report UCRL-ID 122786). Lawrence Livermore National Laboratory. Google Scholar
  7. Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874. CrossRefGoogle Scholar
  8. Ford, L. R., & Fulkerson, D. R. (1956). Maximal flow through a network. Canadian Journal of Mathematics, 8(3), 339–404. Google Scholar
  9. Hochbaum, D. S. (2008). The pseudoflow algorithm: A new algorithm for the maximum-flow problem. Operations Research, 56(4), 992–1009. CrossRefGoogle Scholar
  10. Hochbaum, D. S. (2009). The multi-sensor nuclear threat detection problem. In J. W. Chinneck, B. Kristjansson, & M. J. Saltzman (Eds.), Operations research/computer science interfaces series : Vol. 47. Operations research and cyber-infrastructure (pp. 389–399). New York: Springer. CrossRefGoogle Scholar
  11. Nemzek, R. J., Dreicer, J. S., Torney, D. C., & Warnock, T. T. (2004). Distributed sensor networks for detection of mobile radioactive sources. IEEE Transactions on Nuclear Science, 51(4), 1693–1700. CrossRefGoogle Scholar
  12. Papoulis, A. (1975). A new algorithm in spectral analysis and band-limited extrapolation. IEEE Transactions on Circuits and Systems, 22(9), 735–742. CrossRefGoogle Scholar
  13. Schaller, B. (2006). The New York city taxicab fact book (Technical report). Schaller Consulting, 94 Windsor Place, Brooklyn, NYC, NY, US. Google Scholar
  14. Stephens, D. L., & Peurrung, A. J. (2004). Detection of moving radioactive sources using sensor networks. IEEE Transactions on Nuclear Science, 51(5), 2273–2278. CrossRefGoogle Scholar
  15. UN Scientific Committee on Effects of Atomic Radiation (2000). Report to the general assembly, with scientific annexes, volume I: Sources (Technical report). United Nations General Assembly. Google Scholar
  16. Unger, L. M., & Trubey, D. K. (1982). Specific gamma-ray dose constants for nuclides important to dosimetry and radiological assessment (Technical Report ORNL/RSIC-45/R1). Oak Ridge National Laboratory. Google Scholar
  17. University of California at Berkeley (2009). Domestic nuclear threat security initiative. http://donuts.berkeley.edu.
  18. Yaroslavsky, L. P., Shabat, G., Salomon, B. G., Ideses, I. A., & Fishbain, B. (2009). Nonuniform sampling, image recovery from sparse data and the discrete sampling theorem. Journal of Optical Society of America A, 26(3), 566–575. CrossRefGoogle Scholar

Copyright information

© The Author(s) 2009

Authors and Affiliations

  1. 1.University of CaliforniaBerkeleyUSA

Personalised recommendations